U.S. patent number 8,365,603 [Application Number 12/808,915] was granted by the patent office on 2013-02-05 for non-destructive testing, in particular for pipes during manufacture or in the finished state.
This patent grant is currently assigned to V & M France. The grantee listed for this patent is Bernard Bisiaux, Frederic Lesage, Nidia Alejandra Segura Rodriguez. Invention is credited to Bernard Bisiaux, Frederic Lesage, Nidia Alejandra Segura Rodriguez.
United States Patent |
8,365,603 |
Lesage , et al. |
February 5, 2013 |
Non-destructive testing, in particular for pipes during manufacture
or in the finished state
Abstract
Device forming an operating tool, for the non-destructive
testing of iron and steel products, intended to extract information
on possible imperfections in the product, from feedback signals
that are captured by transmitting ultrasound sensors, receiving
ultrasound sensors forming an arrangement with a selected geometry,
assembled to couple in an ultrasound way with the product via the
intermediary of a liquid medium, with relative rotation/translation
movement between the pipe and the arrangement of transducers, said
operating tool being characterized in that it comprises: a
converter (891; 892) capable of selectively isolating a digital
representation of possible echoes in designated time windows, as a
function of the relative rotation/translation movement, said
representation comprising the amplitude and time of flight of at
least one echo, and of generating a parallelepipedic 3D graph, a
transformer unit (930) capable of generating a 3D image (901; 902)
of possible imperfections in the pipe from the 3D graph and a
database, a filter (921; 922) capable of determining, in the images
(901; 902), presumed imperfection zones (Zcur), and the properties
of each presumed imperfection, and an output stage configured to
generate a product conformity or non-conformity signal.
Inventors: |
Lesage; Frederic (Saint-Saulve,
FR), Segura Rodriguez; Nidia Alejandra (Valenciennes,
FR), Bisiaux; Bernard (Valenciennes, FR) |
Applicant: |
Name |
City |
State |
Country |
Type |
Lesage; Frederic
Segura Rodriguez; Nidia Alejandra
Bisiaux; Bernard |
Saint-Saulve
Valenciennes
Valenciennes |
N/A
N/A
N/A |
FR
FR
FR |
|
|
Assignee: |
V & M France
(Boulogne-Billancourt, FR)
|
Family
ID: |
39636907 |
Appl.
No.: |
12/808,915 |
Filed: |
December 16, 2008 |
PCT
Filed: |
December 16, 2008 |
PCT No.: |
PCT/FR2008/001751 |
371(c)(1),(2),(4) Date: |
June 17, 2010 |
PCT
Pub. No.: |
WO2009/106711 |
PCT
Pub. Date: |
September 03, 2009 |
Prior Publication Data
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|
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Document
Identifier |
Publication Date |
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US 20100307249 A1 |
Dec 9, 2010 |
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Foreign Application Priority Data
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Dec 21, 2007 [FR] |
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07 09045 |
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Current U.S.
Class: |
73/623; 73/638;
73/592; 73/644; 73/622 |
Current CPC
Class: |
G01N
29/0645 (20130101); G01N 29/11 (20130101); G01N
29/4481 (20130101); G01N 29/27 (20130101); G01N
29/4445 (20130101); G01N 2291/011 (20130101); G01N
2291/2634 (20130101); G01N 2291/02854 (20130101); G01N
2291/0234 (20130101); G01N 2291/044 (20130101); G01N
2291/105 (20130101) |
Current International
Class: |
G01N
29/04 (20060101) |
Field of
Search: |
;73/623 |
References Cited
[Referenced By]
U.S. Patent Documents
Foreign Patent Documents
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2 796 153 |
|
Jan 2001 |
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FR |
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10115604 |
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May 1998 |
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JP |
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10311138 |
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Nov 1998 |
|
JP |
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2003 279550 |
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Oct 2003 |
|
JP |
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Other References
Terada, Atsuhiko; Ultrasonic Flaw Detection Evaluating Device; May
1998; English abstract for JP-115604A. cited by examiner .
Terada, Atsuhiko; Ultrasonic Flaw Detection Evaluating Device; May
1998; English translation for JP-115604A. cited by examiner .
Seki, Munemasa; Construction for Identifying single Pipe; Nov.
1998; English abstract for JP-10311138A. cited by examiner .
Seki, Munemasa; Construction for Identifying single Pipe; Nov.
1998; English translation for JP-10311138A. cited by examiner .
Dunlop, I et al., "Automated parameter extraction for ultrasonic
flaw analysis", IEE Proc.-Sci. Meas. Technol., vol. 144, No, 2, pp.
93-99, (Mar. 14, 1997). cited by applicant.
|
Primary Examiner: Khuu; Cindy H
Assistant Examiner: Zhang; Haidong
Attorney, Agent or Firm: Oblon, Spivak, McClelland, Maier
& Neustadt, L.L.P.
Claims
The invention claimed is:
1. A device forming an operating tool, for non-destructive testing,
during or at an end of production, of iron or steel elongated
products, the tool configured to extract information on possible
defects in the product, from feedback signals that are captured,
following selective excitation of transmitting ultrasound sensors
according to a selected time rule, by receiving ultrasound sensors
forming an arrangement with a selected geometry, mounted in
ultrasound coupling with the product via an intermediary of a
liquid medium, with relative rotation/translation movement between
a pipe and the transducer arrangement, the operating tool
comprising: a converter, capable of selectively isolating a digital
representation of possible echoes in designated time windows, as a
function of the relative rotation/translation movement, the
representation including amplitude and time of flight of at least
one echo, and of generating a parallelepipedic three dimensional
(3D) graph; a transformer unit capable of generating a 3D image of
possible defects in the pipe on the basis of the 3D graph and a
database; a filter, capable of determining, in the images, presumed
defect zones, and properties of each presumed defect; and an output
stage configured to generate a product conformity or non-conformity
signal, wherein the transformer unit comprises an unnecessary data
removal element, a pinpointed zones filtering element, a simulator,
and an interpretation unit.
2. A device according to claim 1, wherein the converter comprises a
maximum amplitude in a selector input and a corresponding time of
flight input.
3. A device according to claim 1, wherein the simulator comprises a
theoretical simulation element, a tolerance calculator, and an
inverse algorithm.
4. A device according to claim 1, wherein the output stage
comprises: a combiner, arranged to prepare digital inputs for a
neural circuit, from an extract of the images corresponding to a
presumed defect zone, and properties of the presumed defect in a
same zone, coming from the filter; at least one arrangement of
neural circuit type, that receives inputs from the combiner; a
digital decision and alarm stage, operating on the basis of an
output from the arrangement of the neural circuit type, and a
sorting and marking robot, arranged to separate and mark products
that have been deemed not to conform by the decision and alarm
digital stage.
5. A device according to claim 4, wherein the operating tool
comprises two converters respectively dedicated to two arrangements
of ultrasound transducers with a selected geometry, mounted in
ultrasound coupling roughly according to a mirrored symmetry of the
direction of their respective ultrasound beams, and wherein the
combiner is arranged to operate selectively on inner skin echoes or
on outer skin echoes or the echoes taking place in a mass of the
pipe, but at a same time on data relating to one or other of the
two transducer arrangements.
6. A device according to claim 4, wherein the converter is arranged
to selectively isolate a digital representation of possible echo
maxima in designated time windows corresponding to inner skin
echoes, outer skin echoes, and echoes from a mass of the pipe,
respectively, and wherein the combiner is arranged to operate
selectively on the inner skin echoes or the outer skin echoes or
the echoes occurring in the mass.
7. A device according to claim 4, wherein the combiner receives at
least one input relating to an amplitude extremum of the image in
the presumed defect zone.
8. A device according to claim 4, wherein the filter is arranged to
produce, as properties of each presumed defect, its obliquity and
its length, while the combiner receives corresponding inputs of
defect obliquity and defect length.
9. A device according to claim 4, wherein the filter, the combiner,
the neural circuit, and the digital decision and alarm stage are
arranged to operate iteratively on a series of presumed defect
zones, determined by the filter.
10. A device according to claim 9, wherein the filter, the
combiner, the neural circuit, and the digital decision and alarm
stage are arranged to operate alternately on an inner skin and
outer skin of the pipe.
11. A device according to claim 4, wherein the arrangement of the
neural circuit type comprises: a first neural circuit configured to
evaluate a nature of a defect among a number of predefined classes;
and a second neural circuit configured to evaluate a severity of a
defect.
12. A device according to claim 11, wherein the first and second
neural circuits have inputs that differ by: an input of a number of
maxima in a vicinity for the first neural circuit, and an input of
an echo width for the second neural circuit.
13. A device according to claim 11, wherein the outputs of the
first and second neural circuits are combined to refine a
prediction.
14. A device according to claim 1, wherein the transmission and
reception of the ultrasound signals are performed each time by a
same transducer, for at least part of the arrangement of
sensors.
15. A non-destructive testing device for pipes during or at an end
of production, comprising: an arrangement of ultrasound transducers
with a selected geometry, mounted in ultrasound coupling with a
pipe via the intermediary of a liquid medium, with relative
rotation/translation movement between the pipe and the transducer
arrangement; circuits to selectively excite the transducer elements
according to a selected time rule and to gather feedback signals
captured by the transducer elements; and an operational tool
according to claim 1.
Description
The invention concerns the non-destructive testing of materials,
especially for pipes in the process of manufacture.
Various options, more of which later, are known which tend to use
neural networks in connection with non-destructive testing of
materials. But those currently in existence are unable to operate
in an industrial environment, on equipment already in service, in
real time, whilst allowing a classification on the fly of
imperfections according to their type, in such a way that it is
possible to quickly remedy a problem, arising during the production
phase.
The unpublished French patent application No. 0605923 deals with
non-destructive testing.
An object of the invention is to improve the situation by moving
towards a system that: can be used in an industrial environment and
can be easily installed on equipment that already exists in this
environment; can be used in real time, that is to say can provide
rapid diagnosis, in particular at a speed that is fast enough not
to slow down the overall speed of production, and allows a
classification of imperfections according to their type, on the
basis of a small amount of information, in order to know their
severity and allow a determination of the technical reason for the
imperfection, as well as the rapid remedying of the problem during
the production phase.
According to an initial aspect of the invention, a device is
proposed that forms an operating tool for the non-destructive
testing of pipes (or other iron and steel products) during and at
the end of production. Such a tool is intended to extract
information on possible imperfections in the product. Transmitting
ultrasound sensors are excited selectively according to a selected
time rule. Feedback signals are captured by receiving ultrasound
sensors forming an arrangement with a selected geometry, mounted in
ultrasound coupling with the pipe via the intermediary of a liquid
medium. Finally, there is generally a relative rotation/translation
movement between the product and the transducer arrangement.
The operating tool proposed comprises: a converter, capable of
selectively isolating a digital representation of possible echoes
in designated time windows, as a function of the relative
rotation/translation movement, and extracting from this an image of
possible imperfections in the product, which representation
includes the amplitude and the time of flight of at least one echo,
and of generating a 3D parallelepipedic graph; a transformer unit
capable of generating a 3D image of possible imperfections in the
pipe on the basis of the 3D graph and a database; a filter, capable
of determining, in the images, presumed imperfection zones, as well
as the properties of each presumed imperfection; an output stage
configured to generate a product conformity or non-conformity
signal.
The invention is equally at home a non-destructive testing device
for pipes (or other iron and steel products) during or at the end
of production, which comprises: an arrangement of ultrasound
transducers with a selected geometry, mounted in ultrasound
coupling with the pipe via the intermediary of a coupling medium,
with relative rotation/translation movement between the pipe and
the transducer arrangement; circuits to selectively excite these
transducer elements according to a selected time rule, and for
gathering the feedback signals they capture, and an operating tool
as defined above.
Another aspect of the invention manifests itself in the form of a
non-destructive testing procedure for pipes (or other iron and
steel products) during or at the end of production, comprising the
following stages: a. providing an arrangement of ultrasound
transducers with a selected geometry, mounted in ultrasound
coupling with the pipe via the intermediary of a coupling medium,
with relative rotation/translation movement between the pipe and
the transducer arrangement; b. selectively exciting these
transducer elements according to a selected time rule; c. gathering
the feedback signals they capture, in order to selectively analyse
these feedback signals, so as to extract information on any
imperfections in the pipe, said information including the amplitude
and the time of flight of at least one echo, and generating a 3D
parallelepipedic graph; d. selectively isolating a digital
representation of possible echoes in designated time windows, as a
function of the relative rotation/translation movement, and
extracting from this a 3D image of possible imperfections in the
pipe on the basis of the 3D parallelepipedic graph and a database;
e. generating a product conformity or non-conformity signal.
Step e may comprise: e1. filtering the images according to selected
filter criteria, in order to determine presumed imperfection zones
(Zcur) there, and the properties of each presumed imperfection; e2.
forming working digital inputs, from an extract of the images
corresponding to a presumed imperfection zone (Zcur), properties of
the presumed imperfection in the same zone, coming from the filter,
and contextual data; e3. applying the inputs so formed to at least
one arrangement of the neural circuit type; e4. digitally
processing the output from the arrangement of the neural circuit
type according to selected decision criteria, in order to draw from
this a decision and/or an alarm, and e5. separating and marking
pipes considered not to conform by stage e4.
Other aspects, characteristics and advantages of the invention will
become apparent upon examination of the detailed description that
follows of various non-restrictive embodiments and the attached
drawings, in which:
FIG. 1 is a schematic perspective view of a pipe with imperfections
or defects so-called reference imperfections or reference
defects;
FIG. 2 is a schematic side view illustrating an example of an
installation of the "rotating head testing" type on a pipe at the
end of production;
FIGS. 3A to 3C are details of various types of thickness
measurement and longitudinal and transverse imperfection
testing;
FIG. 4 is the schematic view of the electronics associated with an
ultrasound sensor in non-destructive testing in a conventional
installation;
FIGS. 5A and 5B are an end view and a side view of a particular
type of non-destructive testing cell, commonly known as a "rotating
head" and shown schematically;
FIG. 6 shows the complexity of the ultrasound trajectories
encountered in a pipe, in a simple example;
FIGS. 6A and 6B are schematic timing diagrams of ultrasound
signals, for a sensor under oblique incidence and for a sensor
under normal (perpendicular) incidence, respectively;
FIG. 7 is a graph showing a conventional representation of the
selectivity of a testing installation;
FIG. 8 is a schematic view of the electronics associated with an
ultrasound sensor in non-destructive testing in an example of an
installation capable of implementing the invention;
FIG. 8A is a more detailed block diagram of part of FIG. 8;
FIG. 8B is another more detailed block diagram of part of FIG.
8;
FIG. 9 is a schematised screen shot showing two digitised
ultrasound images of potential imperfections in a pipe;
FIG. 9A is a screen shot from a different angle;
FIGS. 10A to 10D are schematic representations of various types of
imperfections according to the American Petroleum Institute (API)
classification and which constitute the output data from the neural
network tending to determine the type of imperfection;
FIG. 11 is a more detailed block diagram of another part of FIG.
8;
FIG. 11A is a detailed view of the transformer unit of FIG. 11;
FIG. 12 is a sequence chart illustrating the processing of
successive potential imperfections in an image;
FIG. 13 is a block diagram of a system of filters;
FIG. 14 is a block diagram of a neural network setup tending to
determine the type of imperfection in a pipe;
FIG. 15 is a block diagram of a neural network setup tending to
determine the degree of severity of an imperfection in a pipe;
FIG. 16 is a block diagram of the neuron model;
FIG. 17 is an example of an elementary neuron transfer function;
and
FIG. 18 is the general diagram of an installation for the detection
of defects using various types of sensors.
The drawings contain elements of a definite nature. They can
therefore not only serve to better understand the present invention
but can also contribute to its definition, as necessary.
In the remainder of this text, an ultrasound sensor may be referred
to without distinction as a sensor, or probe or transducer, all of
which are well-known to a person skilled in the art.
Neural Networks
The use of neural networks in connection with non-destructive
testing of materials has been the subject of numerous publications,
mostly quite theoretical, which will be considered now.
The article entitled `Localization and Shape Classification of
Defects using the Finite Element Method and the Neural Networks` by
ZAOUI, MARCHAND and RAZEK (NDT.NET--AUGUST 1999, Vol. IV, abridged
Number 8) formulates proposals in this area. However, these
proposals are made in the context of activities in the laboratory,
and the application described does not allow implementation in the
production line of an industrial environment. Furthermore, only the
detection by Eddy currents is dealt with, which is often
inadequate.
The article entitled `Automatic Detection of Defects in Industrial
Ultrasound Images using a Neural Network` by Lawson and Parker
(Proc. of Int. Symposium on Lasers, Optics, and Vision for
Productivity in Manufacturing I (Vision Systems: Applications),
June 1996, Proc. of SPIE vol. 2786, pages 37-47 1996), describes
the application of image processing and neural networks to the
so-called scan TOFD interpretation. The TOFD (Time of Flight
Diffraction) method consists of pinpointing the positions of the
ultrasound sensor where it is possible to observe a diffraction of
the beam at the edges of the imperfection, which allows subsequent
dimensioning of the imperfection. This method is difficult to adapt
to existing non-destructive testing equipment, particularly in an
industrial environment.
The article entitled `Shape Classification of Flaw Indications in
3-Dimensional Ultrasonic Images` by Dunlop and McNab (IEE
Proceedings--Science, Measurement and Technology--July 1995--Volume
142, Issue 4, pages 307-312) concerns diagnostics in relation to
pipeline corrosion. The system allows in-depth non-destructive
testing and allows a three-dimensional study in real time. However,
the system is very slow. This makes its use in an industrial
environment relatively difficult.
The article entitled `Application of neuro-fuzzy techniques in oil
pipelines ultrasonic non-destructive testing` by Ravanbod
(NDT&E International 38 (2005), pages 643-653) suggests that
the imperfection detection algorithms can be improved by the use of
fuzzy logic elements, in combination with the neural network. Here
again, however, the techniques studied concern the inspection of
pipeline imperfections and diagnosis of corrosion
imperfections.
DE 42 01 502 C2 describes a method for creating a signal intended
for a neural network but provides little or no information on the
interpretation of the results, in diagnostics terms. Furthermore,
once again, only detection by Eddy currents is dealt with.
Japanese patent publication 11-002626 concerns the detection of
longitudinal imperfections only, and solely by Eddy currents.
Patent publication No. 08-110323 limits itself to a study of the
frequency of the signals obtained by ultrasound.
Patent publication No. 2003-279550 describes a program for
differentiating between a zone qualified as good and a bad zone of
a product using a neural network. This program goes no further, and
allows neither the classification nor the localisation of
imperfections. As a consequence, the application of this program
may frequently lead to the rejection of parts that would be deemed
good if the results had been interpreted by a human operator.
Non-Destructive Testing of Pipes
The following detailed description is provided essentially in the
context of non-destructive testing of pipes as they leave
production, but without this being restrictive.
As indicated in FIG. 1, the imperfections in a pipe T can be
identified according to their position. So, surface imperfections,
internal or external, include longitudinal imperfections LD, and
circumferential (or transverse or crosswise or transversal)
imperfections CD and oblique or inclined imperfections ID; by
various arrangements of sensors, an attempt is made to detect these
as soon as they extend beyond a length and a depth defined
according to the standards or specifications or customer
requirements (for example, an imperfection length value mentioned
in the standards is 1/2 inch, or approximately 12.7 mm, with a
depth of approximately 5% of the thickness of the product tested).
Imperfections meeting these criteria are called defects.
Imperfections "in the wall" are also of interest, that is to say in
the mass MD (not visible in FIG. 1), which often correspond to
inclusions and split ends, the detection of which is attempted at
the same time as the thickness measurement. The ultrasound beams
are shown diverging in FIG. 1 in order to explain the detection of
imperfections. In practice they will be quite convergent, as will
be seen.
Conventionally, in non-destructive testing by ultrasounds, one of
the following three types of installations is used: so-called
`rotating head` installations, so-called `rotating pipe`
installations, and multi-element encircling sensor installations,
all of which are well-known to a person skilled in the art. In the
case of the use of sensors that operate by electronic scanning, the
relative pipe/sensors rotation is virtual. When used here, the
expression `relative rotation/translation movement between the pipe
and the transducer arrangement` covers the case where the relative
rotation is virtual.
In FIG. 2, the rotating head non-destructive testing machine
comprises an ultrasound device, properly so-called, mounted on a
water enclosure, or water box 100, which crosses the pipe T at a
speed of v=0.5 meters per second, for example. The ultrasound
sensors or probes emit longitudinal waves in the water. A given
sensor works, for example, at 1 or a few MHz. It is excited,
repeatedly, by pulses of a selected waveform, at a rate (or
frequency) of recurrence Fr, also known as pulse repetition
frequency (PRF), which is of the order of a few kHz or tens of kHz,
for example 10 kHz.
Moreover, an ultrasound transducer has: a near-field radiation,
practically parallel, in a so-called Fresnel zone, home to numerous
interferences, whose length along the axis of the beam is
N=0.25D.sup.2/.lamda. where D is the diameter of the active pad of
the transducer, and .lamda. its working wavelength, and a far-field
radiation, in the so-called Fraunhofer zone, according to a
divergent beam of angle 2.alpha., with sin
.alpha.=1.22.lamda./D
FIGS. 3A, 3B and 3C represent sensors made to converge by means of
a concave (ultrasound) lens, as currently used in pipe testing
applications. The Fraunhofer zone is preferably used as there is
less disturbance there.
So, for sensors such as P11 and P12, the ultrasound beam, which is
generally in focus, extends to the vicinity of a plane
perpendicular to the axis of the pipe T. Detection is therefore
carried out noticeably in cross-section. Their roles are as
follows: either their beam is also perpendicular to the axis of the
pipe T in the cross-section, and they serve to measure the
thickness (for example P1, FIG. 3A); this is then referred to as
"straight probing".; or their beam has an incidence on the axis of
the pipe T, in cross-section, and they serve to detect the
longitudinal imperfections (for example P11, FIG. 3B). In this case
the angle of incidence in the cross-section is preferably selected
in order to generate in the pipe only transversal or shear
ultrasound waves, bearing in mind the characteristics of the
water/metal interface of the pipe (in principle water/steel).
Generally two sensors are provided, P11 and P12, with opposing
incidences in relation to the axis of the pipe (FIG. 2).
The machine also comprises sensors such as P21 and P22, the
ultrasound beam of which, also in focus, on the other hand extends
to the vicinity of a plane passing through the axis of the pipe,
but has an incidence in relation to the plane perpendicular to the
axis of the pipe T (see sensor P21, FIG. 3C). In this case, the
angle of incidence in relation to the plane perpendicular to the
axis of the pipe is preferably chosen in order to generate in the
pipe only transversal or shear ultrasound waves, bearing in mind
the characteristics of the water/metal interface of the pipe (in
principle water/steel). These sensors serve to detect the
transversal imperfections. Generally two sensors are provided, P21
and P22, with opposing incidences in relation to the perpendicular
plane of the axis of the pipe (FIG. 2).
Testing for imperfections generally takes place by focusing the
beam. The focal point is measured in relation to the bond, which
corresponds to the first outgoing and return trajectory of the
ultrasounds in the thickness of the pipe. So, the sensor in FIG. 3A
is focused at half-bond, while the sensors of FIGS. 3B and 3C are
focused at three-quarters bond. Moreover, the testing for external
imperfections generally takes place at the bond, and that for
internal imperfections at the half-bond.
Ta is noted, this being the time required for the probe to be able
to correctly receive the return ultrasound beam representing a
possible imperfection. This time Ta depends on the sum of the
following two times: firstly the outgoing and return propagation
time of longitudinal ultrasound waves, over the height of the water
column present between the probe and the pipe, along the trajectory
of the ultrasounds; and secondly the propagation time of
transversal ultrasound waves, as required within the pipe to
perform the non-destructive testing itself. This time depends
mainly on the selected number of reflections of the transversal
waves within the wall of the pipe.
Conventionally, the probes are made to rotate around the axis of
the pipe by means that are not shown, at a speed T of the order of
several thousand revolutions per minute (6,000 rpm, for example).
In the case, also known to a person skilled in the art, where it is
the pipe that is rotated while the probes are not made to rotate
(so-called rotating pipe installation), the speed of rotation of
the pipe is of the order of between several tens and several
thousands of revolutions per minute.
A cell is the name given to each sensor--transmission medium
(water)--pipe assembly. For a cell, consideration must also be
given to the beam opening Od of the detecting ultrasound probes. An
opening can be defined with two components (FIG. 1), one Od1 in the
cross-section of the pipe, and the other Od2 in the plane passing
through the axis of the pipe and the probe.
Adjustment of the installation (as a function of the speed of
rotation, the throughput speed, the dimensions Od1 and Od2 and the
number of probes) should guarantee scanning by the ultrasound beams
of all the surfaces and volume of the pipe to be tested.
It should be noted that certain standards or customer requirements
or specifications state what the coverage of the scanned zones must
be.
The analysis time Ta is therefore defined by a compromise between:
the rate (or frequency) of recurrence Fr, also known as pulse
repetition frequency (PRF); in the cross-section of the pipe, the
speed of rotation w, taking into account the detection opening Od1
of the ultrasound probes (in other words, bearing in mind the
rotation of the sensors, the component Od1 of the beam opening must
allow a time for the presence of the imperfection in front of the
sensors that is at least equal to Ta); along the pipe, the speed of
throughput v of this, bearing in mind the detection opening Od2 of
an ultrasound probe, and the number NFi of probes dedicated to the
same function Fi (which therefore constitute a group of probes),
around the periphery of the pipe (in other words, bearing in mind
the feed of the pipe, the component Od2 of the beam opening must
allow a time for the presence of the imperfection in front of the
sensor (or the group of sensors) that is at least equal to Ta); the
number of probes dedicated to the same role (that is to say the
same function), and the wave propagation times as defined
previously.
Conventionally, the machine typically comprises a total of two
sensors such as P11, P12 for testing for LD type and possibly ID
type imperfections, two sensors such as P21, P22 for testing for
type CD imperfections, plus in principle one sensor of type P1, to
measure the thickness of the product and test for type MD
imperfections. Each sensor may in fact be a group of sensors
working together, as will be seen.
The machine has either integrated or separate excitation and
detection electronics associated with each of the sensors. It
comprises (FIG. 4) a pulse transmitter 70, for example at 250
Volts, for excitation of the probe P0 mounted on the water box 100.
As an integral part of the non-destructive testing system, the
ultrasound probe P0, here a transceiver, receives the echoes
following this excitation. Lines 700 and 710 transmit,
respectively, the excitation pulse and the signal at the terminals
of the probe to an amplifier 73.
The output from the amplifier 73 serves as a display for the
operator and/or control of a sorting robot able to separate
(downstream) non-conform pipes.
The display is, for example, performed on an oscilloscope 750,
which receives as a signal the output from the amplifier 73, and as
a time base 752 a signal from a synchronisation stage 753 coming
from the transmitter 70. A threshold stage 754 avoids blinding of
the oscilloscope at the time of the transmission pulse.
Another output from the amplifier 73 goes to a signal processing
stage 760. This processing generally comprises rectification,
smoothing and filtering. It is followed by a detection or selector
phase 762, capable of isolating significant echoes in a known way.
For detection of the imperfection, it is the presence of an echo,
and its amplitude or its duration (thus its energy), which are
significant, in certain time windows, essentially the half-bond and
the bond. For detection of thickness, a check is made that the
distance equivalent of the time deviation between the respective
bottom echoes correctly corresponds to the desired thickness of the
pipe. Anomalies detected according to these criteria can be used to
issue an alarm in 764, and/or to control a sorting robot 766 which
removes the non-conform pipes, marking these as a function of the
anomaly or anomalies detected.
Physically in the case of a rotating head installation (FIGS. 5A
and 5B), the cell also comprises, on a mechanical support 80, the
water box 100, which houses a sensor assembly P0, with a connection
701, that joins the lines 700 and 710 of FIG. 4. Three rolling
bearings 81 to 83 are, for example, provided in order to centre the
pipe T.
According to the known method (machine sold, for example, by the
German company GE NUTRONIK, formerly NUKEM), the sensor assembly P0
comprises sensors that rotate thousands of times per minute around
the pipe. A number of sensors can also be used distributed in a
ring around the pipe. The ring comprises, for example, 6 sectors of
128 ultrasound sensors, distributed around the periphery. The
sensor sectors have an alternating slight offset in the direction
of the axis of the pipe. This allows coverage between two
consecutive sensor sectors longitudinally and also reduces the
problems of interference.
Interference occurs when a given sensor receives echoes due to a
firing (ultrasonic shot) made on another sensor.
In addition to this there is a bench (not shown) for guiding the
pipe upstream and downstream of the non-destructive testing
station, in order to accurately position the pipe which passes
continuously past the ultrasound sensors.
The non-destructive testing must be performed around the entire
periphery of the pipe. But it is also essential that this test
monitors the linear speed v of the pipe as it leaves production. A
compromise is therefore arrived at between the linear speed v of
the pipe, the rate (or frequency) of recurrence Fr, also known as
pulse repetition frequency (PRF), the analysis time Ta, the working
opening Od of the ultrasound probe during detection, and the speed
of rotation .omega., the number of sensors performing the same
function and the speed of propagation of the ultrasound waves.
It is also desirable if the same installation is able to work
across a full range of pipe diameters (and also pipe thicknesses),
covering the production range. It is then common to provide several
values of the speed of rotation .omega., and frequency of
recurrence Fr, also known as pulse repetition frequency (PRF),
which values are selected as a function of the diameter of the pipe
to be processed.
Finally, it will be noted that any change to production will
involve a readjustment of the angles of incidence of the
ultrasounds of each sensor on the periphery of the pipe. This
delicate operation, which is performed manually, currently takes
around half an hour, during which time production of pipes is
halted. Such are the conditions under which non-destructive testing
by ultrasounds of pipes or other profiled and/or thin-walled
products as they leave production currently takes place.
In the area of ultrasound non-destructive testing, the following
terminology is often employed: "scan" means a sequence of relative
pipe/sensor positions; "increment" means the scanning pitch
(inversely proportional to the frequency of recurrence, also known
as pulse repetition frequency (PRF), or the ultrasound firing
(shot) frequency); "Ascan" means the graph of the electrical
voltage measured at the terminals of an ultrasound sensor, with
time of flight on the abscissa and a representation of the
electrical voltage, also referred to as ultrasound amplitude, on
the ordinate; "Bscan" means an image relative to a given value of
the increment, with the scan corresponding to the ultrasound firing
(shot), possibly expressed in degrees as the angle of the sensor in
relation to the part to be inspected, on the abscissa, and the time
of flight on the ordinate, and at each point the ultrasound
amplitude converted to grey or colour scale; "Echodynamic" means a
curve (graph) with an indication on the abscissa of the ultrasound
firing (shot) and on the ordinate the maximum amplitude detected in
a time selector of the Ascan for the corresponding firing (shot);
"Cscan" means an image with, on the abscissa and the ordinate, the
equivalent position in a flat space of the point (scan position) of
firing (shot) of the ultrasound wave and representing, converted
into grey scale, the maximum ultrasound amplitude for this firing
(shot) detected in the time selector considered of the Ascan (image
amplitude). In the case of a pipe, a point on the abscissa of the
Cscan corresponds to a position on the length of the pipe and a
point on the ordinate to a position on the circumference of the
pipe. In the case of a flat product, a point on the abscissa of the
Cscan corresponds to a position on the length of the flat product
and a point on the ordinate to a position on the width of the flat
product.
Furthermore, the applicant uses in the remainder of the
specification the following terms: "parallelepipedic 3D Bscan"
which designates a 3D representation comprising in addition the
position of the sensor on the axis of the pipe, the representation
being considered as rough and the form of the tube not appearing;
"reduced 3D Bscan" which designates a parallelepipedic 3D Bscan
limited to a zone with an ultrasound indication of a probable
defect at the end of the filtrations; "pipe 3D Bscan" which has the
same dimensions as the parallelepipedic 3D Bscan, the data being
represented in the pipe inspected, the amplitude possibly being
able to constitute a supplementary dimension.
FIG. 6 is a schematic longitudinal cross-sectional view of a system
comprising a sensor, its water column and the pipe, showing the
various ultrasound trajectories forming echoes. It allows a good
understanding of the complexity of these trajectories and the
difficulty of the analysis.
FIG. 6A is a schematic amplitude/time diagram of the ultrasound
signal at the level of a sensor working under oblique incidence.
From the instant Texcit of excitation of the sensor, there is a
water-pipe interface echo at instant Tinterf (which can also be
referred to as TphiExter0). Then there is marking (vertical dotted
line) of the instant TphiInter when the ultrasound beam reaches the
inner skin of the pipe, where it reflects and refracts, as well as
the instant TphiExter1 when the ultrasound beam reaches the outer
skin of the pipe. As a result of the oblique incidence, there is no
significant reflected echo that returns to the sensor in TphiInter
in the absence of an imperfection at this spot. This also applies
at TphiExter1.
FIG. 6B is a schematic amplitude/time diagram of the ultrasound
signal at the level of a sensor working under normal incidence. The
general chronology of the signals is the same as for FIG. 6A
(except for a factor associated with the incidence). On the other
hand, under normal incidence, there are significant echoes in
TphiInter and in TphiExter1, even in the absence of an imperfection
at the points of the pipe concerned.
The present day non destructive testing systems used in the
production of pipes operate by establishing a ratio K between: the
amplitude As of a signal coming from the pipe to be inspected, and
the amplitude A0 of the signal coming from a standard reference
defect, for the type of test concerned. This "standard reference
defect" is in general defined on a reference pipe carrying an
artificial defect (for example a U- or V-shaped notch) with
selected dimensional characteristics, for example in accordance
with a non-destructive testing standard and/or customer
requirements.
The implied assumption is that this signal amplitude is
proportional to the criticality of the imperfection, i.e. to its
depth (DD). The graph of FIG. 7 (well known to a person skilled in
the art, see Nondestructive Testing Handbook--statistics section of
volume 7 published by the ASNT--American Society for Nondestructive
Testing) represents the real distribution K=f(DD). It shows that in
reality the correlation is very poor (of the order of 0.3 to 0.4
for ultrasound testing).
More specifically, in the graph of FIG. 7, if the reference
amplitude A0 (K=1) is fixed at the value XL (maximum acceptable
depth of imperfection) at the centre of the distribution (itself
centred on the oblique TDis), it can be seen that imperfections can
still be found at K=0.5 with a depth DD of greater than XL. It
follows that, to be on the safe side, it is necessary to set A0 at
a much lower value than XL. As a consequence, in production, pipes
will be discarded which, however, would in fact be satisfactory.
This is all the more disastrous, economically, as pipe manufacture
involves heavy engineering which is both complex and
energy-intensive.
The applicant has therefore devoted much effort to improving the
situation.
FIG. 8 shows an improved device compared to that of FIG. 4.
The output of the amplifier 73 is applied to a stage 761, which
digitises the amplitude of the signal coming from the amplifier 73,
and works on this digitised signal. This processing will be
described in the following by reference to FIG. 11. Stages 764 and
766 which are functionally similar to those of FIG. 4 can then be
retained. The raw signal of the sensor, as can be seen on the
oscilloscope 750, is referred to as Ascan by persons skilled in the
art. It includes echoes according to the diagram defined by FIG.
6.
It is desirable to perform imaging of the pipe imperfections with
the help of ultrasound signals. A description is now provided of
how an image is obtained.
In practice an image is obtained by considering several successive
scans of the pipe by a sensor Px, under successive angles which
roughly cover a cross-section of the pipe. It is possible to do
this by successive firings (shots) from a single sensor, using the
relative rotation of the pipe/sensor.
By way of example, and without being restrictive, it is a case here
of an installation of the so-called rotating head type.
In FIG. 8A, a sensor Px is considered, which can be one of the
types P1, P11, P12, P21 and P22 mentioned above. In the example
shown, this sensor Px comprises in fact n elementary sensors Px-1,
Px-i, Px-n, which are aligned along the longitudinal axis of the
pipe, and which are the object of an ultrasound firing (shot) at
the same time. In FIG. 8A, that which is between the elementary
sensors and the 3D graph of output 769 can be considered to be a
converter.
The Ascan signal from the first elementary sensor Px-1 is applied
to an amplifier 73-1, followed by two parallel channels: that of
selector 763-1A and that of selector 763-1B. Each selector 763-1A
comprises two outputs of the maximum amplitude and time of flight
respectively. The maximum amplitude output is connected to a line
digitiser 765-1A. The time of flight output is connected to a line
digitiser 765-1At.
The output of the line digitiser 765-1Aa of the maximum amplitude
is connected to a data buffer store 768-Aa that collects the data
coming from the maximum amplitude line digitisers with an index i
that runs from 1 to n. The output of line digitiser 765-1At of the
time of flight is connected to a data buffer store 768-At that
collects the data coming from the time of flight line digitisers
765-iAt with an index i that runs from 1 to n. The output of the
line digitiser 765-1Ba of the maximum amplitude is connected to a
data buffer store 768-Ba that collects the data coming from the
maximum amplitude line digitisers 765-iBa with an index i that runs
from 1 to n. The output of line digitiser 765-1Bt of the time of
flight is connected to a data buffer store 768-Bt that collects the
data coming from the time of flight line digitisers 765-iBt with an
index i that runs from 1 to n.
On the basis of the information obtained as the reference pipe is
passed through, the operator can enter in the buffer stores 768-Aa
ad 768-At the information T_1A corresponding to an indication of
the position and the time width, which provides it, as a function
of the known geometry of the pipe, with the instants where he will
find an "inner skin echo", relating to the inside of the pipe, for
example the first echo Intl of FIG. 6. FIG. 6A shows more clearly
the corresponding time window "Int", around TphiInter.
Similarly, on the basis of information obtained as the reference
pipe passes through, the operator can enter in the buffer stores
768-Ba and 768-Bt the information T_1B corresponding to an
indication of the position and the time width, which provides it,
as a function of the known geometry of the pipe, with the instants
where he will find an "outer skin echo" relating to the outside of
the pipe, for example the first echo Ext1 of FIG. 6. FIG. 6A shows
more clearly the corresponding time window "Ext", around
TphiExter.
The diagram is repeated for the other sensors Px-2, . . . , Px-i, .
. . Px-n.
So, each time selector 761 defines time windows taking into account
the instant of transmission of the ultrasounds, and pre-definable
time intervals where there can be expected to be echoes concerning
this selector. The illustration of FIG. 6 shows how it is possible
to define the time intervals of interest, taking into account the
angle of incidence of the ultrasound beam on the pipe, as well as
the diameter (internal or external) and the thickness of the pipe.
A given time interval corresponds to a given echo at a given point
of the pipe, for a given relative position between the pipe and the
sensor.
For simplification, it is assumed here that the firing (shot)
instants are synchronised with the relative rotation of the
pipe/sensors, so that an elementary sensor always works on the same
longitudinal generating line of the pipe. The output of its
selector thus provides a spaced out succession of analogue signal
samples, which each correspond to the amplitude of an echo expected
on a wall of the pipe. These samples of sensor Px-1 (for example)
are digitised in 765.
Synchronisation with the transmission can be ensured by a link (not
shown) with the transmitter 70, or with its trigger, the
synchronisation circuit 753, or its time base 752 (FIG. 8). The
display 750 can be maintained, if desired. The system can function
on a pipe rotating at roughly constant speed. In this case, the
angular speed and the feed of the pipe can be measured with the
help of an accurate angle encoder, for example model RS0550168
supplied by the Hengstler company, and a laser velocimeter, for
example model LSV 065 supplied by the company Polytec. The pipe may
also not be rotational, whereas the system of sensors turns. In
this case, the laser velocimeter is sufficient for measuring the
feed of the pipe, while the speed of rotation of the sensors is
known by means of an angle encoder.
For a given firing (shot), the set of sensors Px-1 to Px-n provides
an image line that corresponds to a cross-section of the pipe. In
the other dimension of the image, a given elementary sensor
provides a line which corresponds to a generating line of the
pipe.
The digitisers 765-1Aa, 765-2Aa, . . . , 765-iAa, . . . , 765-nAa
and 765-1At, 765-2At, . . . , 765-iAt, . . . , 765-nAt allow an
"internal" image, relating to the inner skin of the pipe to be
filled. The digitisers 765-1Ba, 765-2Ba, . . . , 765-iBa, . . . ,
765-nBa and 765-1Bt, 765-2Bt, . . . , 765-iBt, . . . , 765-nBt
allow an "external" image, relating to the outer skin of the pipe
to be filled, with Tvol max being the time of flight of the maximum
amplitude echo.
The parallelepipedic 3D graph stored in 769 constitutes the sensor
or group of sensors Px concerned. Each point of this image
corresponds, transposed into shades of grey, to a value of the
amplitude of the echo due to the reflection of the ultrasound
signal on a possible imperfection in the zone of the pipe
concerned. This value can also represent the ratio between the
maximum amplitude of the ultrasound signal captured on the pipe
during the test and the maximum amplitude of the ultrasound signal
obtained with an artificial "standard reference defect", as defined
above. The parallelepipedic 3D graph is a representation of the
preparatory 3D Bscan digitised in 769--preparatory in the sense
that it serves as the basis for generation of the pipe 3D Bscan.
The form of the 3D graph is generally different from the form of
the product examined, in particular for pipes.
The data of the parallelepipedic 3D graph can comprise the set of
pairings (time of flight, amplitude) of the Ascan curve (graph)
over a given digitisation period.
The parallelepipedic 3D graphs digitised in 769 comprise the
parallelepipedic 3D graphs 891 constructed from the data
originating from a group of sensors P11 and the parallelepipedic
graphs 892 constructed from the data originating from a group of
sensors P12 and P21 and P22 respectively as shown in FIG. 11.
This image now corresponds to a zone of the pipe, obtained by
joining together roughly annular zones of the pipe corresponding to
each of the digitised lines. In fact, it is a case of annular or
helical zones if the ultrasound beam is applied roughly
perpendicularly to the axis of the pipe. It is known that the case
differs according to the relative movement of the pipe/sensor. The
zones are then rather more elliptical and, as a result, warped or
twisted in space. In the present description, the expression
"annular zones" covers these various possibilities.
It should be noted that in order to obtain this complete
restoration of the 3D graph, the additional information on the
positioning of the sensor in relation to the pipe is required. It
is available on a separate input 740. This information comes from
an encoder or a set of lasers allowing measurement of the spatial
position. As the pipe can be likened to a cylinder without any
thickness, the positional information can be reduced to two
dimensions.
It is understood that the implementation of the invention on an
existing ultrasound test bench involves: accessibility to the
ultrasound testing raw data, which is provided, for example, with
the help of a data acquisition card, such as model NI 6024, series
E or NI 6251, series M, from the company National Instrument, or by
direct access to the digital data contained in the bench test
electronics; availability of on-line information on the speed of
rotation (of the pipe or of the sensor head) or the relative
angular position of the pipe in relation to the sensor, and
availability of on-line information on the pipe feed speed or the
relative linear position of the sensor projected onto the axis.
The diagram of FIG. 8A can be applied: in parallel to a sensor of
type P11 and a sensor of type P12, observing the same zone of the
pipe from two different directions. Each sensor will allow an
internal image and an external image to be obtained. Then, one of
the images may be selected as a function of a command with the
notation "Int/Ext"; in parallel to a sensor of type P21 and a
sensor of type P22, which, here again, will each allow an internal
image and an external image to be obtained.
The diagram in FIG. 8A can also be applied to a sensor of type P1,
in which case three parallel channels are provided behind each
amplifier (at least virtually). One of these channels operates in a
repetitive time window positioned as indicated under "Volum." in
FIG. 6B. This channel allows a check of imperfections in volume,
that is to say in the thickness of the pipe.
The two other channels can operate respectively in repetitive time
windows positioned as shown in "WphiExter0" and in "WphiInter1" in
FIG. 6B. These two other channels allow measurement of the
thickness of the pipe.
The distinction between the 3 channels is purely functional
(virtual). In fact, the aforementioned two other channels can be
physically the same, in which there is discrimination of the
instants or windows "WphiExter0" and "WphInter1". It is also
possible to use a single physical channel, in which there is
discrimination of the instants or windows "WphiExter0", "Volum."
and "WphiInter1".
It is representative to describe in more detail the case of a
sensor of type P11 with a sensor of type P12. This is what will be
done now.
It will be recalled that these two groups of sensors P11 and P12
are used for detection of longitudinal imperfections in pipes.
Ultrasound testing is performed with ultrasound firings (US shots)
in two preferred directions (clockwise--counter-clockwise): a
sensor or group of sensors P11 provides an ultrasound image of the
pipe in a working direction (clockwise); a second sensor or group
of sensors P12 provides an ultrasound image of the same pipe in
another working direction (counter-clockwise).
So the longitudinal imperfections are advantageously detected with
2 sensors or groups of sensors whose beam axes are inclined
symmetrically in relation to a plane perpendicular to the axis of
the pipe. The inclination is, for example, approximately
.+-.17.degree.. This provides an example of the application of the
system with two sensors, or two groups of sensors, as mentioned
above.
In the embodiment of FIG. 8B, each digitisation window 782 deriving
from an amplifier 781 can be characterised by a start, a duration
and a digitisation frequency that define a number n of points of
the Ascan signal considered. Each digitisation window 782 then
provides a number n of pairings of information (amplitude, time of
flight) for each ultrasound firing (shot). The buffer/multiplexer
788 places all the data collected in this way into the
parallelepipedic 3D graph 769 taking into account the respective
positions of the sensors at the moment when the signal is received,
all at once thanks to knowledge of the geometrical configuration of
the sensors in relation to one another and thanks to the
information on the pipe/sensor positioning at the time of the
ultrasound firing (shot) 740.
Reference is now made to FIG. 9. For the first test direction
("direction 1" tab selected), images 903 and 904 are
cross-sectional views (transversal and longitudinal, respectively)
of the pipe 3D Bscan, 3D with the geometry of the pipe, as
described further on, coming from the sensors P11. The positioning
of these cross-sections is fixed using the "transversal
cross-section at (mm)" and "longitudinal cross-section (degrees)".
The images 905 (internal) and 906 (external) are Cscans, as defined
above, image 905 (or 906) being concentrated on a time zone of the
Ascan where the imperfections in the inner (or outer) skin are
supposed to have been detected. The information necessary for
reconstruction of the images 905 and 906 come from the
parallelepipedic 3D Bscan 891 of FIG. 11.
The image 901 is a 3D representation projected onto the pipe 3D
Bscan of a portion of the product to be tested, in which position
the zones of potential interest are identified, as described
further on. The same images 903 bis, 904 bis, 905 bis, 906 bis and
902 are recreated for the second test direction ("direction 2" tab
active), see FIG. 9A.
We would reiterate at this point that the above description
concerns the detection of defects with a longitudinal orientation.
The same approach applies to the investigation of transversal
defects (with groups of sensors P21 and P22).
Reference is now made to FIG. 11. The image blocks 901 and 902 are
obtained from the parallelepipedic 3D graphs 891 and 892 by means
of transformer unit 930 as detailed in FIG. 11A. The converter unit
891 of FIG. 11 corresponds to the set-up of FIG. 8A, applied to the
sensor P11. Similarly, the converter unit 892 also corresponds to
the set-up of FIG. 8A, but applied to sensor P12. The converter
blocks 891 and 892 use the pipe/sensors contextual data of block
740. These data relate to the characteristics of the pipe under
examination and the sensors currently in use.
Transformer unit 930 is arranged downstream of the parallelepipedic
3D graphs 891 and 892 and can have the structure shown in FIG. 11A.
The transformer unit 930 performs a time calculation of the passage
of the wave propagation in the pipe taking into account the mode
conversion at the time of impact of an ultrasound on a defect. Upon
impact a transversal wave can be transformed into a longitudinal
wave and vice versa. The transformer unit 930 can estimate the
propagation of power of the acoustic beam from calculations of
transmission and reflection coefficients. An analysis of the
frequency spectrum of the Ascan can be carried out. The transformer
unit 930 can comprise a database 939 of real or simulated tests to
allow a comparison with the 3D graphs received. The transformer
unit 930 can recreate the 3D Bscan image with the geometry of the
pipe.
As illustrated in FIG. 11, the transformer unit 930 comprises two
units 931 and 932 for removing unnecessary zones of 3D Bscans from
a 3D graph, unit 931 processing data from the 3D images 891 and
unit 932 processing 3D images 892, two units 933 and 934 for
filtering by application of a simulated time window, downstream,
respectively, of units 931, 932, a theoretical simulation unit 935,
and a tolerance calculation unit 937 supplying an inverse algorithm
unit 936, unit 936 providing the images 901 and 902 defined
above.
The removal by units 931 and 932 allows a reduction in the quantity
of information processed, while retaining zones of potential
interest to be shown in three dimensions. The filtering can be
performed by length on the basis of a Cscan. The length selected
may be greater than the length of a zone with an amplitude greater
than a threshold. The parallelepipedic 3D Bscans including a zone
with a potential imperfection can then be processed.
Filtering by units 933 and 934 can be performed by demarcating the
time window by the interface and bottom echoes. These filter units
can also demarcate the angular zone of the pipe of potential
interest and if necessary offset these zones in order to define and
fully recreate the zone of potential interest. The images provided
by units 933 and 934 are reduced 3D Bscans.
The theoretical simulation unit 935 can comprise a simulations
database, for example of 3D Ascans or Bscans as a function of the
types and position of the defects. The database can comprise
simulated results and/or results from tests on natural and/or
artificial defects.
The inverse algorithm unit 936 can compare theoretical 3D Ascans or
Bscans provided by the theoretical simulation unit 935 and 3D
Ascans or Bscans obtained during the inspection in order to
determine the closest theoretical Ascan or Bscan and, as a
consequence, the most likely defect(s). By way of example, the
inverse algorithm unit 936 compares a filtered experimental Ascan
corresponding to a length position and to an angular position with
theoretical Ascans on this same position in length and evolute. By
way of example, the inverse algorithm unit 936 compares a 3D Bscan
resulting from a reduced 3D Bscan corresponding to a length
position with the theoretical 3D Bscans on this same length
position. The two comparisons can be made. The best set of
theoretical representations of the echoes is then the set that has
the smallest deviations from the experimental data.
After transformer unit 930, filters 921 and 922 are shown, see FIG.
11, which in particular allow extracts to be taken from images, and
from their preparatory data, as input data combined by the combiner
unit 960 for neural or expert processing 970.
In the embodiment described, filter 921 has: a signal output Zcur
designating a working zone in the image. This output is used by an
extraction function 951 which as a consequence performs an
extraction from the image (Cscan) for the Zcur zone, and an access
to the image preparation 891 in order to obtain information stored
there (so-called Ascan), relating to the same Zcur zone. All these
data are transmitted by the extraction function 951 to the combiner
960, as inputs to the neural or expert processing 970; an output
providing information obtained by filtering, some at least relating
to the zone Zcur, which it transmits as input for the neural or
expert processing; optionally (dashed line) outputs of additional
filtered data to a memory 990.
The same applies to filter 922, with the extraction function 952,
for the same Zcur current zone.
The neural system 970 supplies a decision and alarm circuit 992,
which controls a sorting and marking robot 994. An operator
interpretation interface 996 can be provided, which can present all
or part of the data contained in the memory 990, in relation to the
section of pipe under examination. The data contained in the memory
990 come from filters 921 and 922.
Apart from its prediction (origin, type and severity of the
indication) the neural system 970 provides an assessment of the
confidence that can be attached to this prediction. This
information is accessible to operators who also have available more
qualitative data such as the background to the order in progress or
problems that have occurred during construction of the product. The
operator or a specialist can then be involved to weight the
predictions accordingly.
Here, FIG. 11 deals with information coming from at least two
groups of sensors providing the same function or intended for the
same type of testing (the 2 groups P11 and P12 or the 2 groups P21
and P22). The same diagram can be used to handle the information
coming from a larger number of sensor groups intended for different
types of tests. The number of images processed simultaneously is
increased by the same amount.
The primary function of the filters 921 and 922 is to determine the
imperfection zones in the Cscan images 901 and 902. Generally
speaking, the filtering is arranged in order to pinpoint the zones
to be analysed and to distinguish there the imperfections from
other indications. The filtering works on two equivalent portions
of the two images. The two filters can work in conjunction.
By scanning the digital image, to begin with, the areas of the
image are identified where there are potential imperfections. A
fixed threshold established by calibration can be applied for this
purpose.
A threshold can be used that adapts to the prevailing noise level
in the image. The method is based on the theory of the detection of
a signal in a white noise which can be based on two hypotheses:
Hypothesis H0: measurement=white noise of mean m_b and standard
deviation std_b hypothesis H1: measurement=signal+white noise
Statistical tests are performed which allow a determination of
whether the situations fall within the realm of hypothesis H0 or
hypothesis H1. These statistical calculations are performed in real
time on n sliding points of the image corresponding to consecutive
firings (shots). The number n can be determined by learning.
According to this method (so-called Gaussian addition), it is, for
example, possible to use the Neyman-Pearson criterion to determine
a detection threshold according to a given probability of false
alarm (pfa). This is expressed by the attached formula [21]. The
Gaussian cumulative function, generally known as Q (or also the
error function erf) is used, which it is necessary to invert in
order to obtain the threshold, according to the appended formula
[22].
In practice the presence is frequently noted of background noise
that may have various origins (for example: presence of water
inside the pipe, electrical interference, acoustic phenomena due to
the structure of the material of the product under test). The use
of a variable threshold avoids the false alarms that occur if a
fixed threshold is applied.
Among the other false indications that are likely to appear,
interference occurs in the form of very short peaks in the
ultrasound signal. This interference can be removed by simple
algorithms that can be referred to as cumulative counting
algorithms or also integrators (example: "n times before alarm" or
"double threshold").
The applicant has also considered the `turn`, which is the
trajectory followed by the sensor along the cylindrical surface to
which the pipe is likened. Filtering can be performed along each
turn in order to further reduce the rate of false alarms. To this
end use is made, for example of a Butterworth filter and/or a
discrete Fourier transformation, such as a rapid Fourier
transformation. This method is applied to each digital line.
The same type of algorithm can be applied in the longitudinal
direction of the pipe.
In this way potential imperfections are located. Once an
imperfection has been pinpointed its position corresponds to the
position analysed in the images of FIG. 9 (for example), with a 3D
image, a transversal cross-section and an axial cross-section. The
radial position/thickness indications (or, more simply, the
position of the imperfection internally, externally or in the mass)
can be represented as attributes of the points of the image. Thus,
we have: two 2D images representing the possible imperfections in
the outer skin of the pipe; two 2D image representing the possible
imperfections in the inner skin of the pipe, and one 2D image
representing the possible imperfections in the thickness of the
pipe.
The imperfections are now deemed to be "confirmed" following
elimination of interference and false alarms, in particular.
Following on from this the applicant has now decided to work on an
image zone of fixed size. It is therefore necessary to align this
zone with the data on the imperfection existence data that have
just been obtained.
In other words, it is necessary to position the points that have
been identified as being greater than the threshold in order to
determine the complete zone around an imperfection. This is
necessary, for example, if it is desired to determine the obliquity
of an imperfection.
The algorithm goes through a number of steps: contour detection
(Roberts gradient, for example); dilation (gathering of near
contours); erosion, then closure, which allow determination of a
mask around the imperfections; a final surrounding stage allowing
full localisation of the imperfection.
Thus for each imperfection the coordinates are obtained of the
corresponding image zone, which will be useful for the neural
network analysis that takes place next.
FIG. 12 illustrates this processing of the image zones in the form
of a schematic view.
At the start of the images (801), there are between zero and p
image zones to be processed representing a confirmed imperfection.
Operation 803 assumes that there is at least an initial zone, which
serves as the current zone for processing Zcur in 805. For this
zone Zcur: operation 807 selectively extracts data from images 901
and 902 which correspond to this zone (defined by its coordinates
in the image); operation 809 selectively extracts data which have
played a part in the preparation of the images 901 and 902, and
which correspond to zone Zcur. Examples of these data will be
provided below; operation 811 performs the neural processing
properly so-called, more of which later; the results obtained for
zone Zcur are stored selectively in 813, corresponding to a Zcur
zone designation; test 820 looks to see if there is another zone to
be processed in the image, in which case a restart is made in 805
with this other zone as indicated in 821; if not, the processing of
the current image(s) is terminated (822).
In the case of the processing of sensor P1, there is only one
image, which changes the number of input parameters. Apart from
this, the processing can generally be the same.
Following determination of each zone of interest Zcur, the
filtering can comprise other functions. For these other functions,
FIG. 13 illustrates in a schematic way the interaction between the
filtering and the series of operations shown in FIG. 11.
FIG. 13 is similar to FIG. 11, but only for image 901. It shows:
the pipe-sensors contextual elements of block 740; the extractor
951 which finds the data for the Zcur zone, in image 901 and its
preparation 891; an inner/outer block 7410, indicating if the
imperfection in the Zcur zone considered is located in the inner
skin or outer skin.
That added to the base data by the filtering is defined in more
detail, that is, for each Zcur zone (block 805), as shown by the
contents of the box with a dashed line: investigation of the angle
of obliquity in 941; indication of the length of the imperfection
942.
In addition to the following, in particular, may be included: an
alignment indication in Cscan, in 945, and in 946, an indication of
the existence of other imperfections in the same cross-section of
the pipe.
In the embodiment described, the data such as 945 and 946 go to
memory 990. The other data go to the neural networks or expert
systems 970. These are separated here into two functions, as will
now be seen.
An imperfection in the pipe can be defined by its position, its
type and its severity, often likened to its depth. In the
embodiment described, the type and degree of depth of a pipe
imperfection are determined separately with the help of two neural
processes of the same general structure, which will be detailed now
using an example.
The case of the imperfection type is dealt with according to FIG.
14, and that of the severity according to FIG. 15.
The types can be defined, for example as illustrated in FIGS. 10A
to 10D. These figures illustrate four types, which represent a
simplified choice compared to the list of imperfections supplied by
the API and which can be caused by pipe construction processes. The
headings in French and English are those used by persons skilled in
the art to designate the type of imperfection. It will be noted
that imperfections types 1 and 3 are straight and those of FIGS. 2
and 4 arc-shaped ("chord").
A correspondence between the actual imperfections and the four
above types can be defined as follows:
TABLE-US-00001 Name in French Name in English Assignment Entaille
Notch TYPE 1 Tapure Crack TYPE 1 Paille/repliure perpendiculaire ou
Seam TYPE 1 droite (laminage) (perpendicular) Paille/repliure
(laminage) Seam (arcuate), TYPE 2 "overlap" Gravelure Sliver TYPE 3
Origine billette Rolled-in-slug TYPE 4 Rayure Gouge TYPE 4
Inclusion Inclusion TYPE 4 Manque de matiere ("defourni") Bore-slug
TYPE 4 Chevauchement/recouvrement/repliure Lap TYPE 4
Here, FIGS. 14 and 15 both use neural circuits with three
intermediate neurons (or "hidden neurons"), referred to as NC121 to
NC123 for FIG. 14 and NC141 to NC143 for FIG. 15.
FIGS. 14 and 15 have a certain number of inputs in common. As an
aid to understanding, the inputs are illustrated using different
types of lines. Double lines indicate that the inputs are multiple,
that is to say repeated for each point of the Zcur zone.
To begin with, in 7410, according to the status considered by the
selectors 763 concerned, information is provided indicating if it
is a case of processing an imperfection located in the inner skin
or outer skin of the wall of the pipe. This information can also be
obtained from the 3D Bscan.
The second category of common input variables includes contextual
variables, coming from block 740 (FIG. 13): in 7401, WT/OD, which
is the ratio of the wall thickness to the pipe diameter; in 7402,
Freq, which is the frequency of operation of the ultrasound probes;
in 7403, ProbDiam, which is the useful diameter of the ultrasound
probes.
The third category of common variables corresponds to the
quantities resulting from the filtering, which can be considered
common to the two sensors 921 and 922 (or more). An average is
taken, for example, of the results from the two sensors, or the
most representative result (maximum/minimum, as the case may be) is
taken. These quantities are the variables in 9201, the obliquity of
the defect, and in 9202, its length. These two variables are easy
to pinpoint in the two images of FIG. 9, which have a mirrored
symmetry.
Reference is now made to FIG. 14 only. The following category of
variables includes variables of different measurements for each of
the two sensors (or groups of sensors), and for each of the Zcur
zones, which is reflected in the drawing by the use of a double
line.
For a first sensor, we have: in 9511, K1, which is the ratio
between the maximum amplitude of the ultrasound signal encountered
in the Zcur zone and in image 901, to the maximum amplitude of the
abovementioned "standard reference defect". In fact, in the
example, the amplitude in each pixel of the image 901 is defined by
this ratio; K1 is then simply the amplitude maximum encountered in
the Zcur zone of image 901; note Pmax1, the point of the Zcur zone
where this maximum is encountered; in 9512, QBE1 which is a
variable of the Cscan referred to as QuantBumpsEchodyn,
representing the number of local maxima encountered in the Zcur
zone of image 901 in the vicinity of point Pmax1 of maximum
amplitude. This number QBE1 is limited to the local maxima
encountered in the vicinity of Pmax1, either side, but without the
signal amplitude falling below a level corresponding to the
background noise. QBE1 will generally take either the value 1 or
the value 2.
These two variables come from image 901, via the extractor 951,
which is shown by the notation 951(901) in the drawing. Added to
this we have: in 9518, RT1 which is a variable representing the
echo rise time in the native ultrasound signal known as Ascan,
(this is the difference between the moment when the signal is at
its maximum and the last previous moment when the signal is at the
level of the background noise commonly expressed in microseconds).
This variable RT1 has previously been measured at the output of the
amplifier 73 concerned (FIG. 8A); it has been stored, for example
in 891, in correspondence to the point of the pipe to which it
relates. It is in this way that it can be selectively retrieved by
the extractor 951. The variable RT1 can now be directly measured by
the operator on the image 903 of FIG. 9, or also on the
parallelepipedic 3D Bscan.
For the second sensor, we have: in 9521, K2, which is defined like
K1, but for image 902 instead of image 901. In the example, K2 is
simply the amplitude maximum encountered in the Zcur zone of image
902; note Pmax2, the point of the Zcur zone where this maximum is
encountered; in 9522, QBE2 is defined like QBE1, but in image 902
instead of image 901, and in the vicinity of Pmax2. There again,
QBE2 will generally take the value 1, or the value 2.
These two variables come from image 902, via the extractor 952.
Added to this we have: in 9528, RT2 which is a variable
representing the echo rise time in the native signal known as
Ascan. As before, this variable RT2 has previously been measured at
the output of the amplifier 73 concerned (FIG. 8A); it has been
stored, for example in 892, in correspondence to the point of the
pipe to which it relates. It is in this way that it can be
selectively retrieved by the extractor 952. The variable RT2 can
now be directly measured by the operator on image 903A of FIG. 9,
or also on the parallelepipedic 3D Bscan.
The final input 958 of the neural network is a constant value,
referred to as ConstantA, which represents a constant determined at
the time of calibration of the model and resulting from
learning.
The output 998 of FIG. 14 is a variable that is indicative of the
type of imperfection and its average inclination (defined as a
function of the type).
The case of the degree of depth (or severity) of the imperfection
is dealt with according to
FIG. 15. The inputs are the same as for FIG. 14, except: for the
first sensor, block 9512 is replaced by a block 9513, which
processes a variable EW_1, or EchodynWidth, which is the width at
mid-height (50%) of the echodynamic waveform, for this first
sensor. This variable EW_1 is drawn from the Cscan; similarly, for
the second sensor, the block 9522 is replaced by a block 9523,
which processes the variable EW_2, or EchodynWidth, which is the
width at mid-height (50%) of the echodynamic waveform for this
second sensor; in 959, the constant, now referred to as ConstantB,
is different; the output 999 is an indication of the severity of
the imperfection, referred to as DD.
It is of interest to note that, in both cases (FIGS. 14 and 15), a
given neural circuit 970 processes an image extract 951 for one of
the groups of ultrasound sensors, as well as an image extract 952
corresponding to the same zone, but originating from another group
of sensors.
The applicant observed that it was possible to obtain highly
satisfactory results, subject to a suitable adjustment of the
parameters of an expert system, for example of the neural circuits,
and possibly the number of these, to optimise the prediction.
Moreover, the applicant found that by a combination of the
information gathered by the various neural networks, it was
possible to further refine the prediction.
Overall, the input parameters of the neural network or of the
expert system are then characteristics of the two 3D images (ratio
of the max amplitude to the reference amplitude, echo width,
orientation of the echo representing the obliquity of the
imperfection, etc.) and of the test (sensor, dimensions of the
pipe, etc.).
The output parameters are the characteristics of the imperfection
(depth, inclination/type). The decision and/or alarm (992) can take
place automatically with the help of selected decision criteria, on
the basis of thresholds, carrying a degree of safety according to
the need. In order to define these thresholds results from the
learning can be used.
Reference is now made to FIG. 16, which is a model of the
elementary neural circuit of FIGS. 14 and 15, for two sensors.
This model comprises an input layer or level IL, which groups
together all the input parameters (often called "input neurons").
In order not to overload the diagram, only three neurons E1 to E3
are shown, plus a constant, which can also be considered to be a
neuron E0. This constant is most often referred to as the "bias".
In practice there are more input neurons, in accordance with FIG.
14 or FIG. 15, as the case may be.
Then at least one hidden layer or level HL is provided, which
comprises k neurons (of which only 2 are shown in order not to
overload the drawing).
Finally comes the output neuron S1, which provides the decision, in
the form of a value representing the importance of an imperfection
in the pipe, for example a longitudinal imperfection. This output
corresponds to block 998 in FIG. 14 and 999 in FIG. 15.
It is of interest to note that the "neuron" constant E0 comes into
play to weight not only the hidden layer or layers HL, but also the
output neuron (output layer, OL).
The general behaviour of a neural circuit as used here is given by
formula [11] of Annex 1, where w.sub.ij is the weight assigned to
the signal Xi present at the input of neuron j.
In the circuit provided for here, an elementary neuron behaves
according to formula [12], as shown diagrammatically in FIG.
17.
The output S1 of FIG. 16 provides an estimated value that
corresponds to formula [13] of Annex 1.
By learning the applicant has adjusted the hidden neurons and their
weights such that the function f is a non-linear, continuous,
derivable and restricted function. The example currently preferred
is the arc-tangent function.
It is known that a neural network determines its coefficients
w.sub.ij, commonly known as synapses, by learning. The learning
must typically involve between 3 and 10 times more examples than
there are weights to be calculated, while correctly covering the
desired range of working conditions.
Starting with examples E.sub.p (p=1 to M), for each example the
deviation D.sub.p is determined between the value S.sub.p given by
the neural circuit and the actual value Rp measured or defined
experimentally. This is what is reflected by formula [14].
The quality of operation of the neural circuit is defined by a
global deviation variable Cg, known as "cost". It can, for example,
be expressed according to formula [15] as a weighted quadratic
global deviation variable.
The learning poses various problems in a case such as that of
testing for imperfections in the pipes, in particular due to the
fact that heavy engineering is involved, as already indicated.
The applicant first conducted an initial learning by simulation. To
this end it is possible to use the CIVA software developed and
marketed by the Atomic Energy Agency in France. This initial
learning allowed the influencing parameters to be pinpointed and
the construction of an initial version of the neural network based
on virtual imperfections. The cost function was optimised.
The applicant then conducted a second learning combining the
results obtained from simulation and artificial imperfections, that
is to say created intentionally on actual pipes. This second
learning allowed construction of a second version of the neural
network, the cost function of which was also optimised.
The applicant then combined the results obtained with the
artificial imperfections, and with a set of imperfections present
on actual pipes, these imperfections being known with accuracy from
measurements performed a posteriori during the production sequence.
This third phase allowed validation of the final version of the
neural network. This version has proved itself operationally for
production monitoring. However, when implemented in a new or
modified installation, it is currently necessary to put it through
a "calibration" using around ten artificial samples covering the
entire range of imperfections to be dealt with. Of course, an
optimisation then follows.
FIGS. 11, 12, 14 and 15 were described in connection with sensors
P11 and P12.
The same principle applies to the group of sensors P1. In this case
there is no image 2 and the network built has less input
parameters, as already indicated. The circuits described for two
sensors may be used for just one, but without input parameters for
the "Image 2" section.
The same principle can also be applied to the two groups of sensors
P21 and P22, in charge of detecting transversal imperfections,
bearing in mind that for this detection the sensors are inclined
(for example by .+-.17.degree.) in a plane passing through the axis
of the pipe.
It will be understood that, in each case, digital processing takes
place of the type defined by FIG. 11, with the exception of
elements 992 to 996. This procedure has a global designation, 763,
in accordance with FIG. 8 where it is followed by blocks 764 and
766.
A set is in this way obtained as shown by FIG. 18, with: for sensor
P1, a procedure 763-1, followed by a decision and alarm phase
764-1; for sensors P11 and P12, a procedure 763-10, followed by a
decision and alarm phase 764-10; for sensors P21 and P22, a
procedure 763-20, followed by a decision and alarm phase 764-20;
the three phases 764-1, 764-10 and 764-20 being interpreted
together by the sorting and alarm robot 767.
A variant of FIG. 18, which is not shown, consists of providing
only one "decision and alarm" phase, making direct use of the
outputs from the three procedures 763-1, 763-10 and 763-20.
The non-destructive testing, properly so-called, takes place "on
the fly", that is to say as the pipe passes through the test
installation. The decision resulting from the processing of the
information described above can also be taken either as the pipe
passes through the test installation (with decision-alarm and
marking "on the fly"); a variant consists of taking this decision
once the entire length of the pipe has been inspected, or even at a
later time (after testing of an entire batch of pipes, for
example), each pipe being referenced/identified (order No. for
example). In this case, it is necessary that the information
obtained is recorded (stored). The recordings can be the subject of
a later analysis by an operator with the authority to take a
decision following analysis of the results that have been recorded
and processed by the neural networks(s).
Of course, given the properties of the neural circuits, it is
possible to combine at least to some extent all the neural networks
(contained in procedures 763-1, 763-10 and 763-20) in a single
neural circuit having all the desired inputs.
The embodiment described makes direct use of neural networks, by
way of example of expert systems. The invention is not limited to
this type of embodiment. Here the expression "arrangement of the
neural circuit type" can cover other non-linear statistical methods
with or without neural circuits.
Generally speaking, the converter can comprise a maximum amplitude
input in a selector and a corresponding time of flight input. These
inputs can provide sufficient data for the decision on whether a
product conforms or not.
The transformer unit can correspond to an unnecessary data removal
element, a pinpointed zones filtering element, a simulator and an
interpretation unit. Reducing the amount of information allows a
higher processing speed.
The simulator can comprise a theoretical simulation element, a
tolerance calculator and an inverse algorithm.
The output stage can comprise: a combiner, arranged to prepare
digital inputs for the neural circuit, from an extract of the
images corresponding to a presumed imperfection zone, properties of
the presumed imperfection in the same zone, coming from the filter,
and contextual data; at least one neural circuit, that receives the
inputs from the combiner; a digital decision and alarm stage,
operating on the basis of the output from the neural circuit, and a
sorting and marking robot, arranged to separate and mark pipes that
have been deemed not to conform by the decision and alarm digital
stage.
The system proposed here has been described in the case of
non-destructive testing in the manufacture of weld-less pipes, a
case to which the invention lends itself particularly well. The
same methods can apply in particular to elongated iron and steel
products which are not necessarily tubular.
In the case of welded pipes or other welded products (such as
sheets or plates), the system also proves to be capable of
determining the limits of the weld seam, and as a result of
locating any imperfections in the weld seam, which it may be
necessary to monitor. For their part, imperfections located outside
the limits of the weld seam, which may correspond to inclusions
already present in the base strip (or product), must be considered
differently.
APPENDIX
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